Multidisciplinary Journal of Science and Technology

Probabilistic forecast model as a time management tool

Anatoliy Babichev

Department of Management and Administration, V. N. Karazin Kharkiv National University, Ukraine

Anna Chkheailo

Department of Management, Business and Professional Communications, V. N. Karazin Kharkiv National University, Ukraine

Lyashenko Vyacheslav

Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine

##semicolon## Model, Forecast, Probability, Banking, Time management

---

सार

Forecasting plays an important role in economic research. This allows you to justify and make informed and effective decisions. For these purposes, various methods, approaches and models can be used. At the same time, among the possible models, we highlight probabilistic models that allow us to take into account individual characteristics of the processes, phenomena, and objects under study. We can also build models with given characteristics, which allow us to plan some developments. At the same time, the process of predictive modeling is important in the allocation of various resources. Here, a special place is occupied by the resource of time, which allows one to effectively influence the redistribution of other resources. Thus, we consider probabilistic forecasting models as a time management tool. Using the example of specific probabilistic characteristics, we justify the construction of a certain probabilistic forecast model. We show the possibility of using such a model in time management. The work also provides a number of diagrams and graphs that allow you to understand the progress of this study.

---

##submission.citations##

De Bruijn, E. J., & Antonides, G. (2022). Poverty and economic decision making: a review of scarcity theory. Theory and Decision, 92(1), 5-37.

Png, I. (2022). Managerial economics. Routledge.

Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and economic development of economy, 17(2), 397-427.

Азаренкова, Г., & Ляшенко, В. (2009). Відношення переваг у порівняльній оцінці діяльності банків. Банківська справа, 5, 65-72.

Ahmad, M. A., & et al.. (2019). Computational complexity of the accessory function setting mechanism in fuzzy intellectual systems. International Journal of Advanced Trends in Computer Science and Engineering, 8(5), 2370-2377.

Kuzemin, O., & Lyashenko, V. Microsituation Concept in GMES Decision Support Systems. Intelligent Data Processing in Global Monitoring for Environment and Security (pр. 217–238). – 2011. – Р. 217-238.

Слюніна, Т. Л., Бережний, Є. Б., & Ляшенко, В. В. (2007). Розвиток вітчизняної мережі банківських установ: особливості та регіональні аспекти. Вісник ХНУ ім. В. Н. Каразіна. Економічна серія, 755. 84–88.

Lyashenko, V. (2014). Efficiency of bank crediting of real sector of economy in the context of separate banking groups: an empirical example from Ukraine. International Journal of Accounting and Economics Studies, 2(2), 74-79.

Куштим, В. В., & Ляшенко, В. В. (2007). Динаміка розвитку банківського сегмента міжнародного фінансового ринку. Фінанси України, 12, 96-105.

Ляшенко В. В. (2007). Интерпретация и анализ статистических данных, описывающих процессы экономической динамики. Бизнес Информ, 9(2), 108-113.

Dobrovolskaya, I., & Lyashenko, V. (2013). Interrelations of banking sectors of European economies as reflected in separate indicators of the dynamics of their cash flows influencing the formation of the resource potential of banks. European Applied Sciences, 1-2, 114-118.

Kots, G. P., & Lyashenko, V. (2012). Banking sectors of the economies of European countries in the representation of statistical interrelation between indices that characterize their development. European Applied Sciences, 1, 461-465.

Kuzemin, A., & Lyashenko, V. (2009). Methods of comparative analysis of banks functioning: classic and new approaches. Information Theories & Applications, 16(4), 384-396.

Kuzemin, A., & et al.. (2008). Analysis of Spatialtemporal Dynamics in the System of Economic Security of Different Subjects of Economic Management. International Journal Information Technologies and Knowledge, 2(3), 234–238.

Vasyurenko, O., & et al.. (2014). Efficiency of lending to natural persons and legal entities by banks of Ukraine: methodology of stochastic frontier analysis. Herald of the National Bank of Ukraine, 1, 5-11.

Vasiurenko, O., & Lyashenko, V. (2020). Wavelet coherence as a tool for retrospective analysis of bank activities. Economy and Forecasting, (2), 43-60.

Fudenberg, D., Kleinberg, J., Liang, A., & Mullainathan, S. (2022). Measuring the completeness of economic models. Journal of Political Economy, 130(4), 956-990.

Venkateswarlu, Y., & et al.. (2022). An efficient outlier detection with deep learning-based financial crisis prediction model in big data environment. Computational Intelligence and Neuroscience, 2022.

Mazur, B. (2017). Probabilistic predictive analysis of business cycle fluctuations in Polish economy. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(3), 435-452.

Anisimov, V. G., & et al.. (2019). Models of forecasting destructive influence risks for information processes in management systems. Информационно-управляющие системы, (5 (102)), 18-23.

Claessens, B. J., & et al.. (2007). A review of the time management literature. Personnel review, 36(2), 255-276.

Waterworth, S. (2003). Time management strategies in nursing practice. Journal of advanced nursing, 43(5), 432-440.

Olawale, Y. A., & Sun, M. (2010). Cost and time control of construction projects: inhibiting factors and mitigating measures in practice. Construction management and economics, 28(5), 509-526.

Yu, S., Yu, X., Shirinzadeh, B., & Man, Z. (2005). Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica, 41(11), 1957-1964.

Zhou, X., Koltun, V., & Krähenbühl, P. (2020, August). Tracking objects as points. In European conference on computer vision (pp. 474-490). Cham: Springer International Publishing.

Cascio, W. F. (2006). The economic impact of employee behaviors on organizational performance. California Management Review, 48(4), 41-59.

Budhwar, P. S., Saini, D. S., & Bhatnagar, J. (2005). Women in management in the new economic environment: The case of India. Asia Pacific business review, 11(2), 179-193.

Melnyk, L. H., & et al.. (2022). Restructuring of socio-economic systems as a component of the formation of the digital economy in Ukraine.

Ozanne, L. K. (2010). Learning to exchange time: benefits and obstacles to time banking. Int J Commun Curr Res, 14, A1.

Valor Martínez, C., & Papaoikonomou, E. (2016). Time banking in Spain. Exploring their structure, management and users profile.

Umamaheswari, S., & Valarmathi, A. (2023). Role of Artificial Intelligence in The Banking Sector. Journal of Survey in Fisheries Sciences, 10(4S), 2841-2849.

Александр, Я., Ляшенко, В. В. (2006). Процедура нечеткой формализации показателей в оценке устойчивого фуекционирования банков. In Fourth International Conference INFORMATION RESEARCH AND APPLICATIONS (p. 155).